On Predicting with Kernel Ridge Regression

  • Hwang, Chang-Ha (Dept. of Statistical Information, Catholic University of Daegu)
  • 발행 : 2003.02.28


Kernel machines are used widely in real-world regression tasks. Kernel ridge regressions(KRR) and support vector machines(SVM) are typical kernel machines. Here, we focus on two types of KRR. One is inductive KRR. The other is transductive KRR. In this paper, we study how differently they work in the interpolation and extrapolation areas. Furthermore, we study prediction interval estimation method for KRR. This turns out to be a reliable and practical measure of prediction interval and is essential in real-world tasks.


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